The emergence of volumetric image acquisition within the field of medical imaging has attracted a large amount of scientific interest in recent years. Many different approaches to segmentation and tracking of deformable models in volumetric datasets have been proposed, including both novel algorithms and extensions of existing algorithms to 3D datasets. The presently known past attempts are, however, limited to offline operation due to the extensive processing requirements of the current methods, even though volumetric acquisition may be performed in real-time with the latest generation of 3D ultrasound technology. Presently, no method for real-time tracking or segmentation of such data is currently available.
The availability of technology for real-time tracking in volumetric datasets would open up possibilities for instant feedback and diagnosis using medical imaging. There is, for instance, a clinical need for real-time monitoring of cardiac function during invasive procedures and intensive care. The automatic tracking of parameters, such as volume, of the main chamber of the heart, the left ventricle (LV), would be one beneficial application of real-time tracking.
Most tracking approaches in 2D echocardiography have been based on traditional deformable models, which facilitate free-form deformation. These methods, however, tend to be too slow for real-time applications and must be initialized close to the LV boundaries. The problem can, however, be made tractable by restricting the allowable deformations to certain predefined modes. This both regularizes the problem to make tracking more robust, and allows for real-time implementations based on sequential state estimation.
This state estimation approach was first presented by Blake et al. A framework for spatiotemporal control in the tracking of visual contours. International Journal of Computer Vision, 11(2):127-145, 1993, which taught the use of a Kalman filter to track B-spline models deformed by linear transforms within a model subspace referred to as shape space. Later, the framework was applied for real-time left ventricular tracking in long-axis 2D echocardiography by Jacob et al. Quantitative regional analysis of myocardial wall motion. Ultrasound in Medicine & Biology, 27(6):773-784, 2001. All these past methods utilize a B-spline representation, deformed by a trained linear principal component analysis (PCA) deformation model in 2D datasets.
A somewhat similar approach (see D. Metaxas and D. Terzopoulos, Shape and Nonrigid Motion Estimation Through Physics-Based Synthesis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(6):580-591, 1993) used a continuous Kalman filter to estimate parameters for a deformable superquadric model using 3D positions of points sampled from diode markers attached to objects. This yielded direct 3D measurements at predefined known points. The present disclosure, however, uses edge detection to perform displacement measurements at regularly sampled intervals in proximity to a predicted contour.